Patch-Based Microorganism Characterization for Complex Samples
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Solution Overview
Problem
The field of microbiological analysis faces challenges in detecting and characterizing microorganisms, particularly bacteria in blood samples, due to their small size and presence in complex matrices, which complicates early detection and requires significant human expertise, and existing AI solutions struggle with the resource barrier of requiring large annotated image datasets.
Innovation Solution
A deep learning architecture for microorganism characterization using a multi-stage neural network approach, where images are subdivided into sub-images and patches, with decreasing annotation strength, allowing training on a reduced dataset for high-performance diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional blood culture methods are used to detect bacteria in blood samples, then detection reliability is improved, but analysis time is excessively long
Solution Approach 1:
The patent replaces the mechanical/biological blood culture system with an optical imaging and machine learning system. Instead of relying on bacterial growth in nutrient media, the invention uses high-resolution microscopy to capture images of blood samples and applies deep learning models to automatically detect and classify bacteria, eliminating the time-consuming culture process while maintaining detection reliability
Solution Approach 2:
The patent performs preliminary actions by pre-training deep learning models on large datasets of annotated microbial images before actual diagnosis. The models are prepared in advance with learned features of various bacteria types, enabling rapid classification during actual blood sample analysis without requiring time-consuming manual expertise during the diagnostic process
2Measurement precision
If high-resolution imaging with large surface area is used to detect early-stage bacteria, then measurement precision is improved, but data dimensionality becomes excessively large
Solution Approach 1:
The patent applies segmentation by dividing the large high-resolution image into multiple smaller patches or regions of interest. This allows the system to process manageable portions of the image independently, reducing computational complexity while maintaining the ability to detect small bacteria at early stages. The segmented approach enables parallel processing and reduces memory requirements
Solution Approach 2:
The patent extracts only the essential features from the high-resolution images using pre-trained deep learning models. Instead of processing the entire high-dimensional image data, the system extracts relevant visual features (such as shape, texture, and structural characteristics of bacteria) that are sufficient for accurate classification, thereby reducing data dimensionality while preserving detection precision
3Measurement precision
If classical deep learning architectures are used for microorganism classification, then prediction accuracy is improved, but training data requirements become excessively large
Solution Approach 1:
The patent performs preliminary action through transfer learning, where deep learning models are pre-trained on large external datasets of annotated microbial images before being applied to the specific blood sample classification task. This pre-training phase allows the models to learn general features of microorganisms from diverse sources, reducing the need for extensive task-specific annotated data while maintaining high prediction accuracy
Solution Approach 2:
The patent changes parameters by adapting pre-trained models to the specific blood sample classification task through fine-tuning with limited annotated data. By adjusting model parameters and architecture to suit the specific application (blood sample analysis), the system achieves high accuracy without requiring the astronomical quantities of annotated images that classical architectures would need from scratch
4Measurement precision
If manual Gram slide analysis by qualified technicians is used, then characterization accuracy is improved, but operational complexity and expertise requirements increase
Solution Approach 1:
The patent replaces the manual analysis system performed by qualified technicians with an automated optical imaging and machine learning system. The deep learning models automatically perform Gram characterization by analyzing images of blood samples, eliminating the need for human experts to manually examine slides while maintaining or improving characterization accuracy through consistent, objective analysis
Solution Approach 2:
The patent enables self-service by designing a system that performs Gram slide analysis autonomously without requiring qualified technicians. The automated system handles image acquisition, processing, classification, and interpretation independently, making the analysis process accessible to laboratories without specialized expertise while maintaining high accuracy through robust machine learning models
Data Source
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AI summary
A method for classifying microorganisms contained in a sample comprises preparing a slide of the sample, acquiring at least one digital image of the slide and implementing by computer a model for predicting the class of microorganisms as a function of the acquired image. According to the invention, said image is subdivided into sub-images and each sub-image is subdivided into patches, and: A. for each patch, applying a microorganism feature extractor, forming a convolutional part of a first convolutional neural network trained on patches being individually annotated by at least one class; B. for each sub-image, applying a second neural network connected to the extractor, comprising an upstream pooling layer and one or more downstream layers comprising a prediction layer of at least one class, and trained on training sub-images globally; and C. for the acquired image: Fathe calculation of a feature vector calculated for the sub-images; Fb the application of a prediction model of at least one class for microorganisms.